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How Green Are the Streets? an Analysis for Central Areas of Chinese Cities Using Tencent Street View

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How Green Are the Streets? an Analysis for Central Areas of Chinese Cities Using Tencent Street View RESEARCH ARTICLE How green are the streets? An analysis for central areas of Chinese cities using Tencent Street View Ying Long1*, Liu Liu2 1 School of Architecture and Hang Lung Center for Real Estate, Tsinghua University, Beijing, China, 2 China Academy of Urban Planning and Design, Shanghai, China * [email protected] Abstract Extensive evidence has revealed that street greenery, as a quality-of-life component, is a1111111111 important for oxygen production, pollutant absorption, and urban heat island effect mitiga- a1111111111 tion. Determining how green our streets are has always been difficult given the time and a1111111111 money consumed using conventional methods. This study proposes an automatic method a1111111111 using an emerging online street-view service to address this issue. This method was used to a1111111111 analyze street greenery in the central areas (28.3 km2 each) of 245 major Chinese cities; this differs from previous studies, which have investigated small areas in a given city. Such a city-system-level study enabled us to detect potential universal laws governing street greenery as well as the impact factors. We collected over one million Tencent Street View OPEN ACCESS pictures and calculated the green view index for each picture. We found the following rules: Citation: Long Y, Liu L (2017) How green are the (1) longer streets in more economically developed and highly administrated cities tended to streets? An analysis for central areas of Chinese be greener; (2) cities in western China tend to have greener streets; and (3) the aggregated cities using Tencent Street View. PLoS ONE 12(2): e0171110. doi:10.1371/journal.pone.0171110 green view indices at the municipal level match with the approved National Garden Cities of China. These findings can prove useful for drafting more appropriate policies regarding plan- Editor: Xiaolei Ma, Beihang University, CHINA ning and engineering practices for street greenery. Received: July 10, 2016 Accepted: January 15, 2017 Published: February 14, 2017 Copyright: © 2017 Long, Liu. This is an open access article distributed under the terms of the 1. Introduction Creative Commons Attribution License, which permits unrestricted use, distribution, and As one of the most prominent colors in nature, green has been an everlasting beloved color of reproduction in any medium, provided the original humans, and the ªgarden cityº advocated by [1] is among the most famous planning theories. author and source are credited. According to [2], green spaces offer significant potential for restoration, correspond to the Data Availability Statement: All relevant data are innate human tendency to focus on life and lifelike processes, and promote behaviors that within the paper. boost well-being; thus, increasing the provision and utilization of urban green spaces can pro- Funding: The first author would like to mote stress reduction, happiness, health, and well-being among humans. As an essential aspect acknowledge the funding of the National Natural of green-city implementation, green coverage at various scalesÐat the block level (green land Science Foundation of China (No. 51408039). area divided by block area), for example, or citywide (total green land area divided by the city's Competing interests: The authors have declared urban land area)Ðis a mandatory element of spatial plans for promoting a high quality of life. that no competing interests exist. As a result of partial planning implementation and the diverse composition of green spaces, PLOS ONE | DOI:10.1371/journal.pone.0171110 February 14, 2017 1 / 18 How green are the streets in Chinese cities? green coverage in planning drawings does not directly correspond to the total greenery in real- ity. This is one reason why visual greenery has been extensively discussed in the research com- munity and is suggested for use in practice. Although not required in spatial plans, street greeneryÐas the focus of this study and a key indicator for evaluating urban form at the city- design levelÐis important for citizens' quality of life (especially for pedestrians in daily life); however, this has not been sufficiently studied due to a lack of fine-scale data. Understanding how green our streets are has never been easy. Using the conventional methods, it is generally time consuming and expensive. To address this issue, we developed an automatic method using a street-view service while also borrowing and modifying ideas from existing studies such as [3±5]. The green color ratio in street views (termed ªgreen view indexº in this paper)Ðwhich reflects objective city (as well as rural in most street-view products) street (and road) landscapesÐwas selected as the proxy for linking with street greenery in this study, which falls under the umbrella of visual greenery studies. Different from online geo- tagged photos, which reflect city images captured subjectively by photographers, street view objectively depicts the true urban landscape. This is another reason why we chose street view to understand street visual greenery in this study. Today in China, academic studies are increasingly using open data from social networks, commercial websites, and official channels to understand city systems and urban structure, as well as human mobility and activity (see [6] for a review). To the best of our knowledge, this is one of the first studies to analyze street greenery in a large number of cities using street view. This paper is organized as follows: To illustrate the research context, section 2 reviews related areas such as visual greenery and using street-view pictures for urban studies. Sections 3 and 4 introduce the study area, data, and research methods. Section 5 presents the research results in various aspectsÐsuch as the overall pattern, intercity rankings and analysis, and intracity pattern analysisÐas well as the validation of the results. In the final section, we dis- cuss potential applications, academic contribution, research biases, and future plans. 2. Literature review 2.1 Using street-view pictures in urban studies Systems like Google Street View and Bing Maps Streetside enable users to virtually visit cities (on the streets or even indoors) by navigating immersive 360Ê panoramas. There are various endeavors related to Google Street View (GSV) image recognition, including 3-D city model construction [7], commercial-entity identification [8], real-time text localization and recogni- tion [9], and layer interpretation for ground, pedestrians, vehicles, buildings, and sky [10]. In addition to these existing studies in the field of computer science, there are related stud- ies in urban geography, regional science, urban studies, and urban planning. Rundle et al. [11] suggest that GSV can be used to audit neighborhood environments by checking the concor- dance between GSV analysis and field surveys. Odgers et al. [12] observed children's neighbor- hoods using GSV and found it to be a reliable and cost-effective tool. Kelly et al. [13] used GSV to audit built environments and also found it to be a reliable method. Hwang and Sampson [14] identified visible clues of neighborhood gentrification using GSV for systematic social observation. Carrasco-Hernandez [15] reconstructed building geometries and urban sky view factors using the GSV image database. In general, street view has proven to be an effective and reliable tool for measuring built environments on various scales, such as streets and neighbor- hoods. The aforementioned studies were all conducted manually by looking at street-view images, not by automatic means. This time-consuming process places constraints on using street view to analyze large geographical areas. We did find an investigation [16] that PLOS ONE | DOI:10.1371/journal.pone.0171110 February 14, 2017 2 / 18 How green are the streets in Chinese cities? combined crowdsourcing techniques with GSV to identify street-level accessibility problems, but this still relied heavily on manual human effort. Based on our review of the use of street view in two general fields (computer science and urban studies), we found the following mismatch. Computer scientists have been developing advanced image recognition algorithms to automatically identify specific objects, texts, or pat- terns from street view. Urban scientists, however, have employed street view manually, without drawing on the latest progress made by computer scientists. Such time-consuming techniques are not easy for urban scientists to overcome. The second author of this paper has proposed a solution that involves automatic cognitive city mapping using geotagged photos (not street- view pictures), drawing on Kevin Lynch's The Image of the City [17±18]. The present study aims to further explore using street-view pictures to automatically and exhaustively analyze/visualize street greenery in our cities, and thus contribute to building a science of cities [19]. 2.2 Understanding visual greenery The effort to bring natural greenery into urban environments has a long history. In the 1850s, Olmsted focused on urban park reform and street design, trying to combine natural environ- ments with urban living spaces [20]. The greenway movement in the late 1980s was a large- scale concept that proposed creating a green network to give people access to open spaces close to where they live and to link rural and urban spaces in the American landscape [21]. Such urban ªgreen constructionsº are mostly valued for their economic or environmental ben- efits. A study of the cooling effects of street greenery at 11 urban sites in Tel-Aviv showed that the shaded area under a canopy plays a key role in alleviating the ªheat islandº effect [22]. Another important contribution of street-level vegetation is that it improves air quality along street canyons, which has been studied by many researchers like [23]. However, aspects of the visual effect or aesthetic amenity of greenery have received less attention. The ratio of greenery as a measurement of the visibility of street greenery, first pro- posed by [24], calibrates the effective ratio for a variety of landscape scenes via different focal distances.
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